code: def __init__(
      self,
      encoded_vector_size=256,
      train_iters=75000,
      stop_on_error=0.0001,
      learning_rate=0.01,
      is_target=False,
      device="",
  ):
      super().__init__(is_target)
      self._stop_on_error = stop_on_error
      self._learning_rate = learning_rate
      self._encoded_vector_size = encoded_vector_size
      self._train_iters = train_iters
      self._input_lang = None
      self._output_lang = None
      self._encoder = None
      self._decoder = None
      if device == "":
          device = default_device
      else:
          device = torch.device(device)
      self.device = device
  
file_path: lightwood/lightwood/encoder/text/rnn.py
imports: from lightwood.encoder.text.helpers.rnn_helpers import *
  from lightwood.encoder.base import BaseEncoder
  from lightwood.helpers.log import log
  import math
label_desc: Use image node_id 1 to create a new RnnEncoder object from inherited base classes: BaseEncoder with example: obj = RnnEncoder(encoded_vector_size, train_iters, stop_on_error, learning_rate, is_target, device)